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<Paper uid="W05-0638">
  <Title>Exploiting Full Parsing Information to Label Semantic Roles Using an Ensemble of ME and SVM via Integer Linear Programming</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The Semantic Role Labeling problem can be formulated as a sentence tagging problem. A sentence can be represented as a sequence of words, as phrases (chunks), or as a parsing tree. The basic units of a sentence are words, phrases, and constituents in these representations, respectively..</Paragraph>
    <Paragraph position="1"> Pradhan et al. (2004) established that Constituentby-Constituent (C-by-C) is better than Phrase-by-Phrase (P-by-P), which is better than Word-by-Word (W-by-W). This is probably because the boundaries of the constituents coincide with the arguments; therefore, C-by-C has the highest argument identification F-score among the three approaches. null In addition, a full parsing tree also provides richer syntactic information than a sequence of chunks or words. Pradhan et al. (2004) compared the seven most common features as well as several features related to the target constituent's parent and sibling constituents. Their experimental results show that using other constituents' information increases the F-score by 6%. Punyakanok et al.</Paragraph>
    <Paragraph position="2"> (2004) represent full parsing information as constraints in integer linear programs. Their experimental results show that using such information increases the argument classification accuracy by 1%.</Paragraph>
    <Paragraph position="3"> In this paper, we not only add more full parsing features to argument classification models, but also represent full parsing information as constraints in integer linear programs (ILP) to resolve label inconsistencies. We also build an ensemble of two argument classification models: Maximum Entropy and SVM by combining their argument classification results and applying them to the above-mentioned ILPs.</Paragraph>
  </Section>
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